A peak detection algorithm and its application to histogram-based image data reduction

https://doi.org/10.1016/0734-189X(90)90161-NGet rights and content

Abstract

A new automatic peak detection algorithm is developed and applied to histogram-based image data reduction (quantization). The algorithm uses a peak detection signal derived either from the image histogram or the cumulative distribution function to locate the peaks in the image histogram. Specifically, the gray levels at which the peaks start, end, and attain their maxima are estimated. To implement data reduction, gray-level thresholds are set between the peaks, and the gray levels at which the peaks attain their maxima are chosen as the quantization levels. The results of using the proposed algorithm for data reduction purposes are presented in the case of various images.

Reference (13)

There are more references available in the full text version of this article.

Cited by (142)

  • Performance analysis of diabetic retinopathy detection using fuzzy entropy multi-level thresholding

    2023, Measurement: Journal of the International Measurement Confederation
  • Development of signal analysis algorithm for apparent soil electrical conductivity sensor

    2021, Biosystems Engineering
    Citation Excerpt :

    In the first routine, the histograms of the S1 and S2 signals were generated. The algorithm proposed by Sezan (1990) was used to determine the peak number of histograms, which was subsequently employed to analyse if the signals had a square waveform because a square waveform signal has exactly two peaks. The two peaks correspond to sets of values with low and high amplitudes, respectively.

View all citing articles on Scopus
View full text